Update README.md

README.md

@@ -34,8 +34,8 @@ You can train the model using [NeMo Aligner](https://github.com/NVIDIA/NeMo-Alig
 
 ## References
 
+* [HelpSteer2](https://arxiv.org/abs/2406.08673)
 * [DPO method](https://arxiv.org/abs/2305.18290)
-* [HelpSteer](https://arxiv.org/abs/2311.09528)
 * [Llama 3: Open Foundation and Instruct Models](https://ai.meta.com/blog/meta-llama-3/) <br>
 * [Meta's Llama 3 Webpage](https://llama.meta.com/llama3/) <br>
 * [Meta's Llama 3 Model Card](https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md) <br>
@@ -208,13 +208,13 @@ E-Mail: [Zhilin Wang](mailto:[email protected])
 If you find this model useful, please cite the following works
 
 ```bibtex
-@misc{wang2023helpsteer,
-      title={HelpSteer: Multi-attribute Helpfulness Dataset for DPO}, 
-      author={Zhilin Wang and Yi Dong and Jiaqi Zeng and Virginia Adams and Makesh Narsimhan Sreedhar and Daniel Egert and Olivier Delalleau and Jane Polak Scowcroft and Neel Kant and Aidan Swope and Oleksii Kuchaiev},
-      year={2023},
-      eprint={2311.09528},
+@misc{wang2024helpsteer2,
+      title={HelpSteer2: Open-source dataset for training top-performing reward models}, 
+      author={Zhilin Wang and Yi Dong and Olivier Delalleau and Jiaqi Zeng and Gerald Shen and Daniel Egert and Jimmy J. Zhang and Makesh Narsimhan Sreedhar and Oleksii Kuchaiev},
+      year={2024},
+      eprint={2406.08673},
       archivePrefix={arXiv},
-      primaryClass={cs.CL}
+      primaryClass={id='cs.CL' full_name='Computation and Language' is_active=True alt_name='cmp-lg' in_archive='cs' is_general=False description='Covers natural language processing. Roughly includes material in ACM Subject Class I.2.7. Note that work on artificial languages (programming languages, logics, formal systems) that does not explicitly address natural-language issues broadly construed (natural-language processing, computational linguistics, speech, text retrieval, etc.) is not appropriate for this area.'}
 }
 ```